{
  "abstract": "To address the significant decline in the accuracy of rolling bearing fault diagnosis under variable working conditions and strong noise, which is caused by feature distribution shift and fault feature submersion, this paper proposes a bearing fault diagnosis method based on the Dual Attention Multi-Scale Convolutional Neural Network (DAMSCNN). First, a parallel multi-scale convolution module with multiple kernel sizes extracts fault features under different receptive fields, overcoming the limitation of fixed receptive fields in single-scale convolution. Second, a cascaded channel-spatial dual attention mechanism is designed following a progressive weighting strategy. A residual connection is embedded in the channel attention branch to highlight fault-sensitive channels and suppress redundant ones. The spatial attention branch fuses global average pooling and max pooling to strengthen fault impulse regions and suppress interference from working condition fluctuations and background noise. Finally, the weighted features are classified by a Softmax classifier. Experiments on the CWRU bearing dataset demonstrate that DAMSCNN achieves a peak diagnostic accuracy of 99.69% in cross-condition tasks and maintains accuracy above 92% under −5 dB SNR, outperforming BiTCN-Transformer, MTFC-T, and CTCA in accuracy, recall, and F1-score, while also offering lower computational cost and faster inference speed.",
  "abstract_shown": true,
  "affiliations": [
    "School of Intelligent Science and Engineering, Xi’an Peihua University, Xi’an, China",
    "Trine Engineering Institute, Shaanxi University of Technology, Hanzhong, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yamei Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaohu Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1m880e4bcvvtp.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358253",
  "fulltext_url": null,
  "id": "1m880e4bcvvtp",
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    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0358253",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2207|Machine Fault Diagnosis Techniques"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Background signal noise"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks"
    ],
    [
      "plos",
      "/Engineering and technology/Electrical engineering/Electrical faults"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Background signal noise"
    ],
    [
      "plos",
      "/Engineering and technology/Signal processing/Signal to noise ratio"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Vibration"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7213923579",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358253&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/1m880e4bcvvtp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0358253",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358253"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Rolling bearing fault diagnosis method based on dual attention multi-scale convolutional network",
  "title_text": "Rolling bearing fault diagnosis method based on dual attention multi-scale convolutional network",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/1m880e4bcvvtp",
  "venue": "PLOS One",
  "versions": []
}